The Value of Digital Technologies in Pharma Supply Chain
There is a significant amount of technology innovation going on in the pharmaceutical supply chain. From drones bringing products to patients in areas where traditional supply chains are constrained, to machine learning algorithms powered by artificial intelligence that help anticipate risks for supply chain managers to mitigate, and even blockchain solutions to track and trace products as they flow through the end-to-end supply chain.
In my work with pharmaceutical companies, I’ve been lucky to have the opportunity to innovate solutions using this technologies. However, the most fulfilling part has been to see how these solutions can drive value for patients by enabling pharmaceutical companies and their supply chain partners to bring medicines from raw materials to finished products at the pharmacy in a reliable, agile, and effective manner.
I recently had the opportunity to discuss this with Sara Castellanos from the Wall Street Journal (see WSJ: Drugmaker to Test Machine Learning to Prevent Drug Shortages) and wanted to share some additional thoughts on the topic with my network.
As these technologies exit the “proof of concept” phase and prove their link to business and patient value, the next challenge emerges: scaling. I observe three characteristics of companies that are able to scale these solutions across their supply chain:
1) Focus on value and alignment with purpose — The companies that really spend the required time aligning technology innovation with solving high-value business problems across their value chain, and align this to the overall purpose of the corporation have higher success in scaling the transformation. This makes sense, since the purpose alignment will energize leadership, employees, and customers; and the value delivery will fund the journey. By the way, value here should be defined broadly including efficiency but also reliability, agility, and sustainability.
2) Human-centered design to the solutions — With all the excitement around AI and ML, I’ve noticed our data science teams sometimes have the tendency to develop solutions that showcase front and center their technological sophistication. However, we use use human-centered design techniques and Agile methodologies to make sure we are developing with the user at the center. Simplicity makes the solutions a pleasure to adopt, and a deep understanding of the business process used to embed the solutions naturally in the workflow makes them impossible not to adopt. It might sound counter-intuitive but all the complexity should be hidden away from the users, from that angle simple is better. Think about it, when we buy something on Amazon we don’t really want to see how they’re recommendation or product comparison engine works, we just want it to work as simply and seamlessly as possible as we complete the task.
3) Equal investment in the solution development and the underlying platform — The risk to avoid here is that all the investment goes into the solution and not to the enablers that need to change to make the solution work. There is a wide spectrum of critical enablers ranging from the technology stack to manage big data and develop machine learning algorithms at scale (like Gamma’s source.ai), to the change management and up-skilling processes required to enable the users to leverage the new solutions, and changes to the operating model such as the breaking of traditional silos in supply chain management.
As we help our clients deliver these transformations at scale we continue to refine the playbook for success. I look forward to see the new wave of leaders in this space and driving high-value and purposeful innovation in digital supply chains.